Global Optimal Image Reconstruction from Blurred Noisy Data by a Bayesian Approach

C. Bruni, R. Bruni, A. De Santis, D. Iacoviello and G. Koch
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C. Bruni: Università di Roma La Sapienza
R. Bruni: Università di Roma La Sapienza
A. De Santis: Università di Roma La Sapienza
D. Iacoviello: Università di Roma La Sapienza
G. Koch: Università di Roma La Sapienza

Journal of Optimization Theory and Applications, 2002, vol. 115, issue 1, No 6, 67-96

Abstract: Abstract In this paper, a procedure is presented which allows the optimal reconstruction of images from blurred noisy data. The procedure relies on a general Bayesian approach, which makes proper use of all the available information. Special attention is devoted to the informative content of the edges; thus, a preprocessing phase is included, with the aim of estimating the jump sizes in the gray level. The optimization phase follows; existence and uniqueness of the solution is secured. The procedure is tested against simple simulated data and real data.

Keywords: Image analysis; global constrained optimization; Bayesian modeling; wavelet processing (search for similar items in EconPapers)
Date: 2002
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Citations: View citations in EconPapers (2)

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DOI: 10.1023/A:1019624913077

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